Optimal Phased-Array Signal Combination For Polyunsaturated Fatty Acids Measurement In Breast Cancer Using Multiple Quantum Coherence MR Spectroscopy At 3T

Abstract

Polyunsaturated fatty acid (PUFA), a key marker in breast cancer, is non-invasively quantifiable using multiple quantum coherence (MQC) magnetic resonance spectroscopy (MRS) at the expense of losing half of the signal. Signal combination for phased array coils provides potential pathways to enhance the signal to noise ratio (SNR), with current algorithms developed for conventional brain MRS. Since PUFA spectra and the biochemical environment in the breast deviate significantly from those in the brain, we set out to identify the optimal algorithm for PUFA in breast cancer. Combination algorithms were compared using PUFA spectra from 17 human breast tumour specimens, 15 healthy female volunteers, and 5 patients with breast cancer on a clinical 3 T MRI scanner. Adaptively Optimised Combination (AOC) yielded the maximum SNR improvement in specimens (median, 39.5%; interquartile range: 35.5–53.2%, p < 0.05), volunteers (82.4 ± 37.4%, p < 0.001), and patients (median, 61%; range: 34–105%, p < 0.05), while independent from voxel volume (rho = 0.125, p = 0.632), PUFA content (rho = 0.256, p = 0.320) or water/fat ratio (rho = 0.353, p = 0.165). Using AOC, acquisition in patients is 1.5 times faster compared to non-noise decorrelated algorithms. Therefore, AOC is the most suitable current algorithm to improve SNR or accelerate the acquisition of PUFA MRS from breast in a clinical setting.

Phased-array combination of 2D MRS for lipid composition quantification in patients with breast cancer

“…WSVD i , eliminating the need for acquiring an additional reference spectrum (typically scan time of 2 min), reduces scan time by 50-70% from 17 to 8 min in tumour and 5 min in peritumoural adipose tissue. Noise decorrelation algorithms outperformed all linear algorithms substantially through the cancellation of correlated noise, as found in 1D MRS studies 13,17 . WSVD performance was not degraded in low SNR spectra acquired from the tumours, in contrast to 1D spectra reported in previous studies 17,18 .…”

“…Noise decorrelation algorithms outperformed all linear algorithms substantially through the cancellation of correlated noise, as found in 1D MRS studies 13,17 . WSVD performance was not degraded in low SNR spectra acquired from the tumours, in contrast to 1D spectra reported in previous studies 17,18 . In high SNR spectra from adipose tissue, WSVD and AOC yielded comparable SNR and outperformed nd-comb, as observed in 1D PUFA spectra 17 .…”

“…In this work, current combination algorithms, designed for 1D MRS, were adapted and evaluated for lipid composition spectra from breast acquired using 2D MRS, with a particular focus on noise decorrelation algorithms. WSVD i was identified as the most effective signal combination approach in 2D MRS, instead of AOC, the optimal algorithm for 1D MRS 13,17 . WSVD i provided maximal SNR improvement in patients (97% in peritumoural adipose tissue, 52% in tumour) and low non-uniformity of 6% and 21% respectively.…”

Optimized truncation to integrate multi‐channel MRS data using rank‐Rsingular value decomposition

“…While we observed significant increases in SNR after FDR correction for nine comparisons, our combination may be most suitable for 1 H metabolites in relatively homogeneous tissue. Previous reports also emphasize that optimal combination from phased array data may depend on the voxel position, MRS sequence, and metabolites of interest 7,13,15,18 . Experimentally, MRS data may not fully satisfy the Roemer conditions due to spatial variations in the receive field, , across the large MRS voxel or from out‐of‐volume signal contamination due to imperfect localization.…”

“…Several spectral combination methods have been proposed to maximize SNR, utilizing both calculated coil sensitivity or field maps and data‐driven approaches 7,12–15 . The seminal paper by Roemer et al proposed a sum‐of‐squares combination method to remove mutual inductance (signal transfer between coupled coil channels) and mutual resistance (due to coupled hardware and resistance from the conducting sample) by quantifying the correlation of noise between the coils 4 .…”